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A Novel Reconstruction Method for Measurement Data Based on MTLS Algorithm.

Tianqi Gu¹, Chenjie Hu¹, Dawei Tang²

¹School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)

|November 17, 2020

Summary

This summary is machine-generated.

This study introduces the Moving Total Least Trimmed Squares (MTLTS) method for robust discrete data reconstruction. MTLTS effectively suppresses outliers and errors, outperforming Moving Least Squares (MLS) and Moving Total Least Squares (MTLS) in accuracy and robustness.

Keywords:

Moving Least Squares outliers reconstruction method surface profile

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Area of Science:

Data analysis and computational geometry
Numerical methods and algorithms
Engineering and applied sciences

Background:

Moving Least Squares (MLS) and Moving Total Least Squares (MTLS) are established methods for discrete data reconstruction.
These methods, while accurate, lack robustness against outliers, leading to distorted local approximations.
Effective processing of outliers is crucial for reliable model reconstruction in industrial applications.

Purpose of the Study:

To propose an improved method, Moving Total Least Trimmed Squares (MTLTS), for robust and accurate data reconstruction.
To address the limitations of MLS and MTLS in handling outliers and measurement errors.
To enhance the reliability of discrete data reconstruction in the presence of noisy data.

Main Methods:

Development of the Moving Total Least Trimmed Squares (MTLTS) algorithm.
Integration of the Total Least Trimmed Squares (TLTS) method within an orthogonal construction framework.
Application of the proposed MTLTS method to numerical simulations and experimental measurements.

Main Results:

The proposed MTLTS method demonstrates superior performance compared to MLS and MTLS.
MTLTS effectively suppresses outliers and random errors in measurement data.
Numerical simulations and experimental results confirm the enhanced robustness and accuracy of MTLTS.

Conclusions:

The Moving Total Least Trimmed Squares (MTLTS) method offers a significant improvement over existing MLS and MTLS techniques.
MTLTS provides more accurate and robust estimations for discrete data reconstruction, especially in the presence of outliers.
The proposed algorithm is a valuable tool for applications requiring reliable data processing and model reconstruction.